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\title{What If My Thesis Is  Gender Classification}   % type title between braces
\author{Guney Kayim}         % type author(s) between braces

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This document contains possible future works if my master thesis subject is \emph{Gender Classification}.

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\section{Introduction}
What is the motivation behind this thesis and what previous work have been done about it. In short, tell what kind of techniques are used. Express where and how is this useful. For what purpose the techniques used here can be used.
\subsection{Previous works on - Gender recognition, Smile detection, Age estimation}
Apply a survey about the topics (Gender recognition, Smile detection, Age estimation) and give some information about them
\subsection{Brand new database!}
Collect face images for gender classification and smile detection. Data collection for age database could not be possible.
\section{Face Normalization and Alignment Techniques}
Give information about the importance of normalization and alignment in this kind of work
\subsection{Photometric normalization techniques}
Illumination Invariance. What kind of techniques can be used?
\subsection{Geometric normalization and alignment techniques}
Remove tilts and align eyes? What kind of techniques can be used?
\section{Feature Extraction Methods} 
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\subsection{2D Features}
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\subsubsection{DCT}
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\subsubsection{PCA}
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\subsubsection{SIFT}
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\subsubsection{Gabor}
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\subsubsection{More?}
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\subsection{3D Features}
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\subsubsection{Like what?!}
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\section{Classification Techniques}

\subsection{Adaptive Boosting}
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\subsection{SVM}
Different kernels can be used. Linear, RBF, Quadratic.
\subsection{Any other classification method can be used?}
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\section{Multi-label classification}
Instead of using 2 binary classifiers - one for gender classification and one for smile detection - , use a single classifier with 4 classes: smiling male, non-smiling male, smiling female, non-smiling female.
\section{Multi-task learning}
Improve classification performance
\section{Experiments \& Results}
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\subsection{Effect of normalization}
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\subsection{Effect of feature selection}
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\subsubsection{Parameter effects}
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\subsection{Effect of classification method}
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\section{Conclusion}
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